To overcome the problems of low efficiency and poor effect when using traditional digital image processing methods to detect bridge cracks， this paper proposes an integrated bridge crack detection method that integrates deep learning YOLOv5 and U-Net3+ algorithms. By adjusting the width and depth parameters and optimizing the bounding box loss function， a crack identification and location model based on the YOLOv5 target detection algorithm is constructed to realize the rapid identification and location of bridge cracks. The U-Net3+ image segmentation algorithm combined with a deep supervision strategy and the output prediction module is introduced to train and build an efficient segmentation model of bridge cracks， and to realize pixels-level intelligent extraction of cracks. An eight-direction crack width measurement method combined with connected domain denoising， edge detection， and morphology processing is developed. And the morphology and width of cracks are measured with high precision based on U-Net3+ segmentation results. LabelImg image annotation tool is used to make a dataset containing 4 414 images to train the crack identification and location model. LabelImg image annotation tool and CFD dataset are used to make a dataset containing 908 images to train the crack segmentation model. UAV is used to capture 485 images of size 5 280×2 970 pixels， which are taken from the bridge tower. The crack images of the bridge tower are used as the test object of the intelligent crack detection model. The proposed crack intelligence detection method is applied to the above test objects， the overall precision， recall， and F1 score of crack identification and location are 91.55%， 95.15%， and 93.32%， respectively， and the overall precision， recall， and F1 score of crack segmentation are 93.02%， 92.22%， and 92.22%， respectively. The results show that the intelligence detection method of bridge cracks based on YOLOv5 and U-Net3+ algorithms can achieve high efficiency， high precision， and intelligence detection of bridge cracks， which has much research value and broad application prospects.